Papers
2
Total Citations
24
H-Index
2
About
Leng Han is a leading researcher in mobile robotics, specializing in autonomous navigation and path planning for complex, dynamic environments. His work focuses on developing hybrid algorithms that fuse global and local planning strategies to ensure collision-free, real-time robot movement. Han’s major contributions include the creation of the MS-W-Theta* algorithm, an enhanced adaptive 3D path planner that integrates obstacle buffering and minimum snap trajectory smoothing, significantly improving path traversability for automated guided vehicles (AGVs) carrying shelves. He also pioneered a hybrid A* and Dynamic Window Approach (DWA) algorithm, solving the critical challenge of unpredictable obstacle avoidance in unknown settings. With over 24 citations across his most-cited papers, Han’s research directly impacts warehouse automation and industrial robotics, offering practical, computationally efficient solutions for real-world deployment. His work is notable for its focus on bridging theoretical path optimization with robust, adaptive real-time performance, making him a key figure in advancing intelligent mobile robot navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2